Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments
With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collecti...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/10/2/109 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849744636287909888 |
|---|---|
| author | Zhengzhe Xiang Fuli Ying Xizi Xue Xiaorui Peng Yufei Zhang |
| author_facet | Zhengzhe Xiang Fuli Ying Xizi Xue Xiaorui Peng Yufei Zhang |
| author_sort | Zhengzhe Xiang |
| collection | DOAJ |
| description | With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face limitations. UAVs have emerged as a promising solution for overcoming these challenges by facilitating data collection and transmission in various environments. However, existing UAV trajectory optimization algorithms often overlook the critical factor of the battery capacity, leading to potential mission failures or safety risks. In this paper, we propose a trajectory planning approach Hyperion that incorporates charging considerations and employs a greedy strategy for decision-making to optimize the trajectory length and energy consumption. By ensuring the UAV’s ability to return to the charging station after data collection, our method enhances task reliability and UAV adaptability in complex environments. |
| format | Article |
| id | doaj-art-806c60b12c434ffebc03e7de6a14edaf |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-806c60b12c434ffebc03e7de6a14edaf2025-08-20T03:12:02ZengMDPI AGBiomimetics2313-76732025-02-0110210910.3390/biomimetics10020109Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex EnvironmentsZhengzhe Xiang0Fuli Ying1Xizi Xue2Xiaorui Peng3Yufei Zhang4Shcool of Computer and Computing Science, Hangzhou City University, Hangzhou 310025, ChinaShcool of Computer and Computing Science, Hangzhou City University, Hangzhou 310025, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310007, ChinaShcool of Computer and Computing Science, Hangzhou City University, Hangzhou 310025, ChinaShcool of Art and Archeology, Hangzhou City University, Hangzhou 310025, ChinaWith the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face limitations. UAVs have emerged as a promising solution for overcoming these challenges by facilitating data collection and transmission in various environments. However, existing UAV trajectory optimization algorithms often overlook the critical factor of the battery capacity, leading to potential mission failures or safety risks. In this paper, we propose a trajectory planning approach Hyperion that incorporates charging considerations and employs a greedy strategy for decision-making to optimize the trajectory length and energy consumption. By ensuring the UAV’s ability to return to the charging station after data collection, our method enhances task reliability and UAV adaptability in complex environments.https://www.mdpi.com/2313-7673/10/2/109trajectory optimizationdata collectionedge computinglow-altitude economy |
| spellingShingle | Zhengzhe Xiang Fuli Ying Xizi Xue Xiaorui Peng Yufei Zhang Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments Biomimetics trajectory optimization data collection edge computing low-altitude economy |
| title | Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments |
| title_full | Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments |
| title_fullStr | Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments |
| title_full_unstemmed | Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments |
| title_short | Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments |
| title_sort | unmanned aerial vehicle trajectory planning for reliable edge data collection in complex environments |
| topic | trajectory optimization data collection edge computing low-altitude economy |
| url | https://www.mdpi.com/2313-7673/10/2/109 |
| work_keys_str_mv | AT zhengzhexiang unmannedaerialvehicletrajectoryplanningforreliableedgedatacollectionincomplexenvironments AT fuliying unmannedaerialvehicletrajectoryplanningforreliableedgedatacollectionincomplexenvironments AT xizixue unmannedaerialvehicletrajectoryplanningforreliableedgedatacollectionincomplexenvironments AT xiaoruipeng unmannedaerialvehicletrajectoryplanningforreliableedgedatacollectionincomplexenvironments AT yufeizhang unmannedaerialvehicletrajectoryplanningforreliableedgedatacollectionincomplexenvironments |